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Why Retailers Can’t Let Data Dilemmas Lead to ‘Emotional Shell Shock’

New, upstart retailers are often lauded for their ability to turn data into dollars, while traditional players are painted as hopelessly out of step. The truth is, established stores do have a lot to learn, but experts say, they’re also positioned to turn small wins into big results—if they don’t let fear of the unknown stand in their way.

“Traditional retailers are the most exciting businesses to work with, because there’s so much low hanging fruit,” said Cecile Lee, head of account management at Trendalytics. Lee explained that established retailers that are often written off as being too outdated or expansive to wield data effectively can usually see meaningful results with a small amount of work.

Jake Pasini, analytics director at Listrak, echoed the sentiment. “You can find one major pain point, and usually tie it to a singular problem,” Pasini said. “Your top one, two or three problems typically have the biggest opportunity for improvement.”

Brands often get hung up on the talking points of “big data,” overwhelmed by the conversation around data analytics and massive players like Amazon. “We see a lot of people have emotional shell-shock around data,” Ben Schein, vice president of the Center for Data Curiosity and Innovation at Domo explained. “You have to get them out of that shock and into conversation with the data, and that starts with teaching them to ask the right questions.” Schein added that starting small, and meeting retailers where they are, is crucial to seeing those data solutions succeed.

In fact, many retail decision-makers are probably more primed to use data effectively than they realize. “A lot of basic machine learning and forecasting algorithms aren’t super complicated,” Schein said, but can be intimidating at first. That is, until brand partners realize they can use their own ideas and observations to produce insights from data tools. Doug Kimball, vice president of global solution strategy at Stibo Systems, used Nike’s reaction to its very public basketball blowout kerfuffle as an example of a massive company using simple stats to make a change. “Because Nike had that ability to get social sentiment data and information on what went wrong, they were able to pivot sooner,” Kimball said.

Succeeding in data science is all about being able to put numbers in context, Pasini said. “Big data isn’t about figuring out someone has a 0.2 percent higher conversion rate on Sundays when it rains in Pennsylvania,” Pasini said. “It’s about asking a question in the right way and looking at the right data.”

Kimball agreed, adding that “leveraging the day-t0-day” is a great way for retailers to get comfortable with their data and deploy it effectively for initiatives big and small. “The easiest place to start, although it’s very broad, is to pick any part of the process that isn’t digitized and digitize it,” Kimball said. “Pick an area. Sales and operations planning is one of the best areas to focus on, because all the factors drive those processes can be heightened when you understand your supply chain and when you understand your customer.”

Schein pointed to e-commerce as a part of the retail landscape that benefits rapidly from data integration, and that opens up doors for forecasting teams and brand experts to guide decisions using their own expertise.

“We have data scientists [at Domo] who work and share their insights with the retail teams, and retailers come back with questions,” Schein said. “We give them more to understand the data, and they keep coming back more knowledgeable, able to see differences in performance and understand their assortments. That’s the conversation you have to unlock.”

Retailers also need to trust that their data has value—even if they don’t understand it. “Sometimes with legacy tech, the issue is you’re so far down a hole that you don’t even know where to begin to make it better,” Lee said. “If your data isn’t clean, that’s okay, we can help you interpret it because we’re always looking to the future.” Even when retailers do have the knowledge to utilize the data they collect, they often don’t have time to study it or conduct broader market research, which is where analytics companies come in. And brands shouldn’t be too worried about approaching data the “wrong” way.

“You don’t need to find a perfect answer,” Pasini said. “Retailers should be using new data sources and new tools to come up with ideas, to test new ways to monetize processes or perform better.” Data is for exploration, he said, and companies that are willing to seek out the right data for their business needs will be sturdier—and in the long run, more profitable—than those that limit the way they gather and apply data.

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